Pattern recognition system, pattern recognition method, and pattern recognition program

ABSTRACT

A pattern recognition system, pattern recognition method, and pattern recognition program capable of increasing the accuracy in computing the false acceptance probability and capable of ensuring a stable security strength are provided. Pattern recognition systems  10  and  10   a  comprise a first probability computation unit  32 , and a second probability computation unit  33  coupled to the first probability computation unit  32 . The first probability computation unit  32  computes a first probability P FCR  based on the number n of corresponding characteristic points cs 1  to csn and cf 1  to cfn indicating points corresponding between characteristic points s 1  to sn s  in a first pattern and characteristic points f 1  to fn f  in a second pattern. The first probability P FCR  indicates the probability of existence of a third pattern that has a greater number of corresponding characteristic points to the first pattern than the number n of the corresponding characteristic points. The second probability computation unit  33  refers to the first probability P FCR  to compute a false acceptance probability P FAR  indicating the probability of falsely determining that the first pattern and the second pattern correspond to each other.

TECHNICAL FIELD

The present invention relates to a pattern recognition method, system,and program, and particularly to a pattern recognition method, system,and program for identifying a person based on data such as voice data orimage data.

BACKGROUND ART

When a fingerprint verification apparatus is used to identify a person,the following process is generally performed. First, the degree ofconsistency (the match rate) between an input fingerprint patternobtained by a sensor or the like and a registered fingerprint patternthat has been registered in advance is computed. Then, the match rate iscompared with a predetermined threshold to determine whether the inputfingerprint pattern and the registered fingerprint pattern are of thesame person. Based on the determination result, the person isidentified. In this personal identification process, the rate at whichan input fingerprint pattern of a person is falsely determined to beidentical with a registered fingerprint pattern of another person iscalled the “false acceptance rate.”

In this type of fingerprint verification apparatus, the predeterminedthreshold is often uniformly fixed to a certain value irrespective ofwhich finger is verified. However, the match rate between thefingerprint patterns actually varies among persons (fingers). That is,some people have fingerprints that provide a high match rate, whileother people have fingerprints that only provide a low match rate.Therefore, if the threshold is set to a higher value, the fingerprintverification apparatus tends to reject authentication of a wrong person,but it also tends to falsely reject authentication of the genuineperson. Conversely, if the threshold is set to a lower value, thefingerprint verification apparatus tends to accept authentication of thegenuine person, but it also tends to falsely accept authentication of awrong person. This will be a cause of reduction in the identificationsuccess rate.

According to a data recognition method disclosed in Patent Document 1(Japanese Patent Application Laid-Open No. 2000-215313), the match ratebetween each registered data item and other registered data items iscomputed. Then, based on a match rate distribution obtained for eachregistered data item, a threshold for the registered data item isgenerated. When a person is identified, the match rate between averification target data to be recognized and a corresponding candidatedata item in the registered data items is computed. The computed matchrate is compared with the threshold for the candidate data item todetermine whether the verification target data and the candidate datacorrespond to each other. In this data recognition method of PatentDocument 1, a target value for the false acceptance probability is givenfirst, and the lowest match rate that meets the target value isdynamically computed as the threshold. Thus, the threshold is differentfor each registered data item.

A pattern recognition apparatus disclosed in Patent Document 2 (JapanesePatent Application Laid-Open No. 2002-230551) involves, for a certainset of patterns, determining a difference between the feature vector ofeach pattern and the average feature vector of each correct category.This produces a set of difference vectors. An error distributioncorresponding to this set of difference vectors is used as a probabilitydensity function to perform pattern recognition.

An object of the present invention is to provide a pattern recognitionsystem, a pattern recognition method, and a pattern recognition programcapable of increasing the accuracy in computing the false acceptanceprobability.

Another object of the present invention is to provide a patternrecognition system, a pattern recognition method, and a patternrecognition program capable of ensuring stable security strength.

Still another object of the present invention is to provide a patternrecognition system, a pattern recognition method, and a patternrecognition program capable of reducing the learning cost of patternrecognition.

DISCLOSURE OF THE INVENTION

The Disclosure of the Invention will be described below using referencenumerals and symbols used in the Best Mode for Carrying Out theInvention. The reference numerals and symbols are added with bracketsfor clarifying the correspondence between the description in the Claimsand the Best Mode for Carrying Out the Invention. However, the referencenumerals and symbols should not be used for interpretation of thetechnical scope of the invention set forth in the Claims.

Pattern recognition systems (10, 10 a) of the present invention comprisemeans for computing a first probability (P_(FCR)) indicating theprobability of existence of a third pattern having a larger number ofcorresponding characteristic points to the first pattern than the number(n) of the corresponding characteristic points, based on the number (n)of corresponding characteristic points (cs1 to csn, cf1 to cfn)indicating points corresponding between characteristic points (s1 tosn_(s)) in a first pattern and characteristic points (f1 to fn_(f)) in asecond pattern. The pattern recognition systems comprise means forcomputing, based on the first probability (P_(FCR)), a false acceptanceprobability (P_(FAR)) indicating the probability of falsely determiningthat the first pattern and the second pattern correspond to each other.

Pattern recognition systems (10, 10 a) of the present invention comprisemeans for determining a first vector (Di) including differences betweenpairs of corresponding characteristic points (cs1 to csn, cf1 to cfn)indicating points corresponding between characteristic points (s1 tosn_(s)) in a first pattern and characteristic points (f1 to fn_(f)) in asecond pattern. This first vector (Di) is used to compute a secondprobability (P_(PRE)) indicating the probability of existence of asecond vector (Ds) having difference components between pairs ofcorresponding characteristic points determined between the first patternand any pattern. This second probability (P_(PRE)) is used to compute afalse acceptance probability (P_(FAR)) indicating the probability offalsely determining that the first pattern and the second patterncorrespond to each other.

In the pattern recognition systems (10, 10 a) of the present invention,the second probability (P_(PRE)) may indicate the probability that themagnitude of the second vector (Ds) is smaller than the magnitude of thefirst vector (Di). The second probability (P_(PRE)) may indicate theprobability that the product of the components of the second vector (Ds)is smaller than the product of the components (d1 to dn) of the firstvector (Di). The second probability (P_(PRE)) may indicate theprobability that each component of the second vector (Ds) is smallerthan the corresponding component (d1 to dn) of the first vector (Di).The second probability (P_(PRE)) may indicate the probability of logicalOR between the event that each component of the second vector (Ds) issmaller than the corresponding component (d1 to dn) of the first vector(Di) and the event that each component of the second vector (Ds) issmaller than the corresponding component of a third vector (Di′). Thethird vector (Di′) has components resulting from rearranging thecomponents (d1 to dn) of the first vector (Di).

In the pattern recognition systems (10, 10 a) of the present invention,a distribution function (p(Ds)) of the second vector (Ds) is uniform.

The pattern recognition systems (10, 10 a) of the present inventionfurther comprise a difference data detection unit (31) coupled to thefirst probability computation unit (32). The difference data detectionunit (31) receives first characteristic data (112) indicating thecharacteristic points (s1 to sn_(s)) in the first pattern and secondcharacteristic data (122) indicating the characteristic points (f1 tofn_(f)) in the second pattern. The difference data detection unit (31)detects the corresponding characteristic points (cs1 to csn, cf1 to cfn)based on the first characteristic data (112) and the secondcharacteristic data (122) and outputs difference data (131) indicatingthe detection result to the first probability computation unit (32). Thedifference data (131) includes the number (ns) of the characteristicpoints in the first pattern, the number (nf) of the characteristicpoints in the second pattern, the number (n) of the correspondingcharacteristic points, and the first vector (Di). The first probabilitycomputation unit (32) computes the first probability (P_(FCR)) based onthe difference data (131). The second probability computation unit (33)is coupled to the difference data detection unit (31) via the firstprobability computation unit (32), so that the difference data (131) isprovided to the second probability computation unit (33).

The pattern recognition system (10 a) of the present invention furthercomprises an identification determination unit (34) coupled to thesecond probability computation unit (33). The identificationdetermination unit (34) receives false acceptance probability data (133)indicating the false acceptance probability (P_(FAR)) from the secondprobability computation unit (33). The identification determination unit(34) compares the false acceptance probability (P_(FAR)) and apredetermined threshold (P_(PFAR)) and outputs a comparison result(134). For example, the identification determination unit (34)determines that the first pattern and the second pattern are identicalif the false acceptance probability (P_(FAR)) is smaller than thepredetermined threshold (P_(PFAR)). When it is determined that the firstpattern and the second pattern are identical, a door is opened accordingto the comparison result (134), for example.

In the pattern recognition systems (10, 10 a) of the present invention,the first pattern and the second pattern are fingerprint patterns, forexample. Thus, the pattern recognition systems (10, 10 a) of the presentinvention may be applied to a fingerprint authentication apparatus.

A pattern recognition method of the present invention comprises thesteps of: (a) computing a first probability (P_(FCR)) based on thenumber (n) of corresponding characteristic points (cs1 to csn, cf1 tocfn) indicating points corresponding between characteristic points (s1to sn_(s)) in a first pattern and characteristic points (f1 to fn_(f))in a second pattern; and (b) referring to the first probability(P_(FCR)) to compute a false acceptance probability (P_(FAR)) indicatingthe probability of falsely determining that the first pattern and thesecond pattern correspond to each other.

In the pattern recognition method of the present invention, thecomputing step (b) comprises the steps of: (b-1) computing a secondprobability (P_(PRE)) indicating the probability that the amount basedon a second vector (Ds) is smaller than the amount based on a firstvector (Di); and (b-2) computing the false acceptance probability(P_(FAR)) based on the first probability (P_(FCR)) and the secondprobability (P_(PRE)). In the computing step (b-2), the false acceptanceprobability (P_(FAR)) is computed by multiplying the first probability(P_(FCR)) and the second probability (P_(PRE)) together, for example.

In the pattern recognition method of the present invention, the secondprobability (P_(PRE)) may indicate the probability that the magnitude ofthe second vector (Ds) is smaller than the magnitude of the first vector(Di). The second probability (P_(PRE)) may indicate the probability thatthe product of the components of the second vector (Ds) is smaller thanthe product of the components (d1 to dn) of the first vector (Di). Thesecond probability (P_(PRE)) may indicate the probability that eachcomponent of the second vector (Ds) is smaller than the correspondingcomponent (d1 to dn) of the first vector (Di). The second probability(P_(PRE)) may indicate the probability of logical OR between the eventthat each component of the second vector (Ds) is smaller than thecorresponding component (d1 to dn) of the first vector (Di) and theevent that each component of the second vector (Ds) is smaller than thecorresponding component of a third vector (Di′).

In the computing step (a) of the pattern recognition method of thepresent invention, the first probability (P_(FCR)) is computed based onthe number (ns) of the characteristic points in the first pattern, thenumber (nf) of the characteristic points in the second pattern, and thenumber (n) of the corresponding characteristic points.

The pattern recognition method of the present invention furthercomprises the steps of: (c) comparing the false acceptance probability(P_(FAR)) and a predetermined threshold (P_(PFAR)); and (d) determiningthat the first pattern and the second pattern are identical if the falseacceptance probability (P_(FAR)) is smaller than the predeterminedthreshold (P_(PFAR)).

A pattern recognition program of the present invention causes a computerto perform the steps of: (A) computing a first probability (P_(FCR))based on the number (n) of corresponding characteristic points (cs1 tocsn, cf1 to cfn) indicating points corresponding between characteristicpoints (s1 to sn_(s)) in a first pattern and characteristic points (f1to fn_(f)) in a second pattern; and (B) referring to the firstprobability (P_(FCR)) to compute a false acceptance probability(P_(FAR)) indicating the probability of falsely determining that thefirst pattern and the second pattern correspond to each other.

In the computing step (B), the pattern recognition program causes thecomputer to perform the steps of: (B-1) computing a second probability(P_(PRE)) indicating the probability that the amount based on a secondvector (Ds) is smaller than the amount based on a first vector (Di); and(B-2) computing the false acceptance probability (P_(FAR)) based on thefirst probability (P_(FCR)) and the second probability (P_(PRE)). In thecomputing step (B-2), the false acceptance probability (P_(FAR)) iscomputed by multiplying the first probability (P_(FCR)) and the secondprobability (P_(PRE)) together, for example.

In the pattern recognition program of the present invention, the secondprobability (P_(PRE)) may indicate the probability that the magnitude ofthe second vector (Ds) is smaller than the magnitude of the first vector(Di). The second probability (P_(PRE)) may indicate the probability thatthe product of the components of the second vector (Ds) is smaller thanthe product of the components (d1 to dn) of the first vector (Di). Thesecond probability (P_(PRE)) may indicate the probability that eachcomponent of the second vector (Ds) is smaller than the correspondingcomponent (d1 to dn) of the first vector (Di). The second probability(P_(PRE)) may indicate the probability of logical OR between the eventthat each component of the second vector (Ds) is smaller than thecorresponding component (d1 to dn) of the first vector (Di) and theevent that each component of the second vector (Ds) is smaller than thecorresponding component of a third vector (Di′).

In the computing step (A), the first probability (P_(FCR)) is computedbased on the number (ns) of the characteristic points in the firstpattern, the number (nf) of the characteristic points in the secondpattern, and the number (n) of the corresponding characteristic points.

The pattern recognition program of the present invention further causesthe computer to perform the steps of: (C) comparing the false acceptanceprobability (P_(FAR)) and a predetermined threshold (P_(PFAR)); and (D)determining that the first pattern and the second pattern are identicalif the false acceptance probability (P_(FAR)) is smaller than thepredetermined threshold (P_(PFAR)).

EFFECT OF THE INVENTION

The pattern recognition system, pattern recognition method, and patternrecognition program according to the present invention allow anincreased accuracy in computing the false acceptance probability.

The pattern recognition system, pattern recognition method, and patternrecognition program according to the present invention allow ensuring ofstable security strength.

The pattern recognition system, pattern recognition method, and patternrecognition program according to the present invention allow a reducedlearning cost of pattern recognition.

BEST MODE FOR CARRYING OUT THE INVENTION

A pattern recognition system, pattern recognition method, and patternrecognition program according to the present invention will be describedwith reference to the appended drawings. In the present invention, thepattern recognition system compares a pattern to be recognized(hereafter referred to as an “input pattern”) with a pattern stored in adatabase (hereafter referred to as a “reference pattern”) and evaluatesthe match rate between the two patterns. Examples of these patternsinclude a person's fingerprint, face, and voiceprint.

First Embodiment

FIG. 1 is a block diagram showing a configuration of the patternrecognition system according to a first embodiment of the presentinvention. In FIG. 1, the pattern recognition system 10 includes apattern input unit 1, a reference pattern input unit 2, a dataprocessing section 3, and an output unit 4. The data processing section3 includes a difference data detection unit 31, a false correspondenceprobability computation unit 32, and a false acceptance probabilitycomputation unit 33. Now, the mechanism and operation of these unitswill be described one by one.

As shown in FIG. 1, the pattern input unit 1 receives pattern data 111representing an input pattern to be recognized. Examples of the patterndata 111 include image data or voice data about a person. For example,the person may put his finger on a fingerprint sensor and inputfingerprint image data (the pattern data 111) representing hisfingerprint (the input pattern) to the pattern input unit 1. The patterninput unit 1 analyzes the pattern data 111 and extracts characteristicquantities of the input pattern. In the case of fingerprint recognition,examples of the characteristic quantities include characteristic pointssuch as endpoints and branch points of fingerprint ridges. The patterninput unit 1 then outputs pattern characteristic data 112 representinginformation on the characteristic quantities (the characteristic points)to the difference data detection unit 31 in the data processing section3.

FIG. 5 is a conceptual view showing a distribution of the characteristicquantities of a certain input pattern. For example, the pattern data 111such as fingerprint image data is analyzed, and as shown in FIG. 5, fourcharacteristic points s1 to s4 are extracted from the image. The numberof characteristic points in the input pattern is generally expressed asn_(s) (n_(s) is a natural number). That is, the input pattern hascharacteristic points s1 to sn_(s). Instead of the pattern data 111,data representing this distribution of the characteristic quantities maybe directly input to the pattern input unit 1.

As shown in FIG. 1, the reference pattern input unit 2 receivesreference pattern data 121 representing a reference pattern. Thereference pattern data 121 is registered in a database in advance. Inauthentication of a person, for example when the person inputs an IDnumber, the reference pattern data 121 corresponding to the ID number isprovided from the database to the reference pattern input unit 2.Examples of the reference pattern data 121 include image data and voicedata about a person. The reference pattern input unit 2 analyzes thereference pattern data 121 and extracts characteristic quantities of thepattern. In the case of fingerprint recognition, examples of thecharacteristic quantities include characteristic points such asendpoints and branch points of fingerprint ridges. The reference patterninput unit 2 then outputs reference pattern characteristic data 122representing information on the characteristic quantities (thecharacteristic points) to the difference data detection unit 31 in thedata processing section 3.

FIG. 6 is a conceptual view showing a distribution of the characteristicquantities of a certain reference pattern. For example, the referencepattern data 121 such as fingerprint image data is analyzed, and asshown in FIG. 6, four characteristic points f1 to f4 are extracted fromthe image. The number of characteristic points in the reference pattern(hereafter referred to as reference characteristic points) is generallyexpressed as n_(f) (n_(f) is a natural number). That is, the referencepattern has reference characteristic points f1 to fn_(f). Instead of thereference pattern data 121, data representing this distribution of thecharacteristic quantities may be directly input to the reference patterninput unit 2.

As shown in FIG. 1, the difference data detection unit 31 receives thepattern characteristic data 112 and the reference pattern characteristicdata 122 from the pattern input unit 1 and the reference pattern inputunit 2 respectively. The difference data detection unit 31 compares thepattern characteristic data 112 and the reference pattern characteristicdata 122 to check the correspondence between the characteristic pointss1 to sn_(s) and the reference characteristic points f1 to fn_(f).

FIG. 7 is a diagram superimposing FIGS. 5 and 6 to conceptuallyillustrate determination of the correspondence between thecharacteristic points by the difference data detection unit 31. In FIG.7, circles depicted by dotted lines indicate predetermined ranges(reference ranges) containing the reference characteristic points f1 tof4 respectively. The difference data detection unit 31 refers to thereference ranges, and if a characteristic point is within a referencerange, it determines that this characteristic point and the referencecharacteristic point in this reference range correspond to each other.In FIG. 7, it is determined that the characteristic point s1 and thereference characteristic point f1, the characteristic point s2 and thereference characteristic point f2, and the characteristic point s3 andthe reference characteristic point f3 correspond to each other,respectively. These corresponding characteristic points and referencecharacteristic points are hereafter referred to as “correspondingcharacteristic points.”

FIG. 8 is a conceptual view showing the corresponding characteristicpoints determined in FIG. 7. As shown in FIG. 8, the input pattern hasthree corresponding characteristic points cs1, cs2, and cs3. Similarly,the reference pattern has three corresponding characteristic points cf1,cf2, and cf3. The corresponding characteristic points cs1, cs2, and cs3correspond to the corresponding characteristic points cf1, cf2, and cf3respectively. Then, the difference data detection unit 31 computes thedifferences in physical quantity for the pairs of correspondingcharacteristic points. For example, the difference data detection unit31 computes the distance d1 between the corresponding characteristicpoints cs1 and cf1, the distance d2 between the correspondingcharacteristic points cs2 and cf2, and the distance d3 between thecorresponding characteristic points cs3 and cf3.

The number of pairs of corresponding characteristic points in the inputpattern and the reference pattern is generally expressed as n (n is anatural number). That is, the input pattern has correspondingcharacteristic points cs1 to csn, and the reference pattern hascorresponding characteristic points cf1 to cfn. The difference datadetection unit 31 then computes the differences d1 to dn in physicalquantity for the pairs of corresponding characteristic points andgenerates input difference data Di=(d1, d2, . . . , dn) indicating thedifferences. The input difference data Di is data representing ann-dimensional vector. For example, the difference data detection unit 31computes the distances d1 to dn between the corresponding characteristicpoints cs1 to csn and the corresponding characteristic points cf1 to cfnrespectively. In the case of fingerprint recognition, the differencedata detection unit 31 may compute the differences of the directions offingerprint ridges at each corresponding characteristic points. Thedifference data detection unit 31 outputs difference data 131 includingthe input difference data Di detected as above to the falsecorrespondence probability computation unit 32 (see FIG. 1).Specifically, the difference data 131 includes the input difference dataDi, the number of the corresponding characteristic points n, the numberof the characteristic points in the input pattern ns, and the number ofthe reference characteristic points nf.

Then, as shown in FIG. 1, the false correspondence probabilitycomputation unit 32 receives the difference data 131 from the differencedata detection unit 31 and computes a false correspondence probabilityP_(FCR). The false correspondence probability P_(FCR) is the probabilityof existence of another reference pattern that has a greater number ofcorresponding characteristic points to the input pattern than theabove-described number of the corresponding characteristic points n.That is, the false correspondence probability P_(FCR) indicates theprobability of m>n, wherein the number of corresponding characteristicpoints between the input pattern being recognized and the referencepattern being recognized is n as described above, and the number ofcorresponding characteristic points between the input pattern beingrecognized and another reference pattern is m. Generally, the greaterthe number of corresponding characteristic points between two patternsunder comparison is, the higher match rate the two patterns have.

The number of the characteristic points in the input pattern ns, thenumber of the reference characteristic points nf, and the number of thecorresponding characteristic points n are provided by the differencedata 131 from the difference data detection unit 31. The probabilitythat characteristic points are falsely determined as corresponding toeach other between different patterns is expressed as p. This p isdetermined in advance such as by a preliminary experiment. Then, thefalse correspondence probability P_(FCR) between the input pattern andthe reference pattern is given by the following formula 1.

$\begin{matrix}{P_{FCR} = {\sum\limits_{k = n}^{\min{({n_{S},n_{f}})}}{\frac{{n_{S}!}{n_{f}!}}{{k!}{\left( {n_{S} - k} \right)!}{\left( {n_{f} - k} \right)!}}\frac{{\Gamma\left( {\frac{1}{p} - n_{S} + 1} \right)}{\Gamma\left( {\frac{1}{p} - n_{S} + 1} \right)}}{{\Gamma\left( {\frac{1}{p} + 1} \right)}{\Gamma\left( {\frac{1}{p} - n_{s} - n_{f} + k + 1} \right)}}}}} & (1)\end{matrix}$

Then, the false correspondence probability computation unit 32 outputsfalse correspondence probability data 132 indicating the computed falsecorrespondence probability P_(FCR), as well as the difference data 131,to the false acceptance probability computation unit 33.

As shown in FIG. 1, the false acceptance probability computation unit 33receives the difference data 131 and the false correspondenceprobability data 132 from the false correspondence probabilitycomputation unit 32. The false acceptance probability computation unit33 then computes a preliminary false acceptance probability P_(PRE)defined as described below.

The input difference data Di=(d1, d2, . . . , dn) computed from thedistribution of the corresponding characteristic points between theinput pattern being recognized and the reference pattern beingrecognized is an n-dimensional vector. Here, the difference dataobtained based on the distribution of n corresponding characteristicpoints between the input pattern being recognized and another referencepattern is referred to as a candidate difference data Ds. This candidatedifference data Ds is also data representing an n-dimensional vector.Then, the preliminary false acceptance probability P_(PRE) is theprobability that a “quantity” based on the candidate difference data Dsis smaller than a “quantity” based on the input difference data Di. Anexample of the quantity is the “magnitude (norm).” In that case, thepreliminary false acceptance probability P_(PRE) means the probabilitythat the norm of the candidate difference data Ds is smaller than thenorm of the input difference data Di. The “quantity” depends on modelsdescribed below.

Now, the technique of computing a false acceptance probability P_(FAR)by the false acceptance probability computation unit 33 will bedescribed in more detail. A probability density function (distributionfunction) of the above-described candidate difference data Ds isexpressed as p(Ds). The p(Ds) is determined in advance by a preliminaryexperiment or assumed to conform to a uniform distribution model. A setof candidate difference data Ds whose “quantity” is smaller than the“quantity” based on the input difference data Di is expressed as R(Di).Then, the preliminary false acceptance probability P_(PRE) between theinput pattern and the reference pattern is given by the followingformula 2.

$\begin{matrix}{P_{PRE} = {\int_{D_{S} \in {R{(D_{i})}}}{p\left( D_{S} \right)}}} & (2)\end{matrix}$

For example, FIG. 9 is a graph that conceptually shows a model of theabove-mentioned set R(Di). It is assumed here that the input differencedata Di is a two-dimensional vector, i.e., Di=(d1, d2). In FIG. 9, the“magnitude (norm)” of the difference data is adopted as theabove-mentioned “quantity.” That is, the set R(Di) includes candidatedifference data Ds whose norm is smaller than the norm of the inputdifference data Di. Thus, the set R(Di) is defined by the shadedcircular area in the figure. The probability density function p(Ds) ofthe candidate difference data Ds is determined by a preliminaryexperiment. Alternatively, it is assumed that the probability densityfunction p(Ds) is uniform. In this case, the preliminary falseacceptance probability P_(PRE) is equal to the size of the circulararea.

FIG. 10 is a graph that conceptually shows another model of theabove-mentioned set R(Di). In FIG. 10, the “product of components” ofthe difference data is adopted as the above-mentioned “quantity.” Thatis, the product of components of candidate difference data Ds includedin the set R(Di) is smaller than the product d1 d 2 of the components ofthe input difference data Di. Thus, the set R(Di) is defined by theshaded area in the figure. It is assumed here that the probabilitydensity function p(Ds) is uniform and all components of the candidatedifference data Ds are within the range of [0,1]. When the preliminaryfalse acceptance probability P_(PRE) in this case is expressed as P₁,the preliminary false acceptance probability P₁ is given by thefollowing formula 3 or 4:P ₁(d ₁ , . . . d _(n))=∫∫_(x) ₁ _(x) ₂ _(. . . x) _(n) _(≦d) ₁ _(d) ₂_(. . . d) _(n) dx ₁ dx ₂ . . . dx _(n)  (3);

$\begin{matrix}{{{P_{1}\left( {d_{1},\ldots\mspace{11mu},d_{n}} \right)} = {\sum\limits_{k = 1}^{n}{\left( {- 1} \right)^{k - 1}\frac{1}{\left( {k - 1} \right)!}{t\left( {\log\mspace{11mu} t} \right)}^{k - 1}}}},} & (4)\end{matrix}$wherein t in the formula 4 is given by t=d1 d 2 . . . dn.

FIG. 11 is a graph that conceptually shows still another model of theabove-mentioned set R(Di). In FIG. 11, the “components” of thedifference data is adopted as the above-mentioned “quantity.” That is,each component of candidate difference data Ds included in the set R(Di)is smaller than the corresponding one of the components d1 and d2 of theinput difference data Di. Thus, the set R(Di) is defined by the shadedrectangular area in the figure. The probability density function p(Ds)of the candidate difference data is determined by a preliminaryexperiment. Alternatively, it is assumed that the probability densityfunction p(Ds) is uniform. In this case, the preliminary falseacceptance probability P_(PRE) is equal to the size of the rectangulararea.

FIG. 12 is a graph that conceptually shows a still another model of theabove-described set R(Di). In FIG. 12, the set R(Di) includes the setR(Di) shown in FIG. 11. The set R(Di) further includes the set R(Di′)determined in the same manner as in FIG. 11, wherein Di′=(d2, d1), i.e.,data resulting from rearranging the components of the input differencedata Di. That is, each component of candidate difference data Dsincluded in the set R(Di) is smaller than the corresponding component ofthe input difference data Di or the above-described data Di′. Thus, theset R(Di) is defined by the shaded area in the figure. This set R(Di) iseffective when all characteristic points have similar nature, such as inthe case of fingerprint patterns. It is assumed here that theprobability density function p(Ds) is uniform and all components of thecandidate difference data Ds are within the range of [0,1]. It isfurther assumed that, when the components d1 to dn of the inputdifference data Di are sorted in order of magnitude, they have therelation |dn′|≦ . . . ≦|d2′|≦|d1′|(n′ is a natural number). When thepreliminary false acceptance probability P_(PRE) in this case isexpressed as P₂, the preliminary false acceptance probability P₂ isgiven by the following formula 5.

$\begin{matrix}{{P_{2}\left( {d_{1},\ldots\mspace{11mu},d_{n}} \right)} = {{n!}{\int_{0}^{f_{n}}\ {{\mathbb{d}x_{n}}\mspace{11mu}\ldots\mspace{11mu}{\int_{x_{2}}^{d_{1}^{\prime}}\ {\mathbb{d}x_{1}}}}}}} & (5)\end{matrix}$

The preliminary false acceptance probability P₂ is also given by thefollowing recursion formulas 6 to 8.P ₂(d ₁ , d ₂ . . . , d _(n))=n!C _(n)  (6)

$\begin{matrix}{C_{n} = {\sum\limits_{k = 1}^{n}{\left( {- 1} \right)^{k - 1}\frac{1}{k!}C_{n - k}d_{n}^{\prime\; k}}}} & (7)\end{matrix}$C₀=1  (8)

Once the preliminary false acceptance probability P_(PRE) is computed inthis manner, the false acceptance probability computation unit 33integrates this preliminary false acceptance probability P_(PRE) and thefalse correspondence probability P_(FCR) from the false correspondenceprobability computation unit 32 to finally compute the false acceptanceprobability P_(FAR). For example, the false acceptance probabilitycomputation unit 33 computes the false acceptance probability P_(FAR) bymultiplying the preliminary false acceptance probability P_(PRE) and thefalse correspondence probability P_(FCR) together. The false acceptanceprobability computation unit 33 then outputs false acceptanceprobability data 133 indicating the computed false acceptanceprobability P_(FAR) to the output unit 4.

As shown in FIG. 1, the output unit 4 receives the false acceptanceprobability data 133 from the false acceptance probability computationunit 33 and outputs result data 141 indicating the false acceptanceprobability P_(FAR). The result data 141 also means the securitystrength of the pattern recognition system 10 and the evaluation valuefor the pattern besides the false acceptance probability of the patternrecognition result.

The false acceptance probability computation unit 33 may be connected toboth the difference data detection unit 31 and the false correspondenceprobability computation unit 32. In that case, the difference datadetection unit 31 provides the difference data 131 to both the falsecorrespondence probability computation unit 32 and the false acceptanceprobability computation unit 33. The false correspondence probabilitycomputation unit 32 provides only the false correspondence probabilitydata 132 to the false acceptance probability computation unit 33.

The above-described processing in the data processing section 3 may beperformed by a computer. That is, the processing in the difference datadetection unit 31, the false correspondence probability computation unit32, and the false acceptance probability computation unit 33 is eachwritten as a computer program and executed by a computer.

FIG. 2 is a flowchart showing the summary of the pattern recognitionmethod according to the first embodiment of the present invention.First, the pattern data 111 is input to the pattern input unit 1 (stepS1). Then, the difference data detection unit 31 detects the differencedata 131 based on the pairs of characteristic quantities of the patterndata 111 and the reference pattern data 112 (step S2). The differencedata 131 includes the number of characteristic points (ns) in the inputpattern, the number of characteristic points (nf) in the referencepattern, the number of corresponding characteristic points (n), and theinput difference data Di=(d1, . . . , dn). Then, the falsecorrespondence probability computation unit 32 receives the differencedata 131 and computes the false correspondence probability P_(FCR) basedon the difference data 131 (step S3). Then, the false acceptanceprobability computation unit 33 receives the difference data 131 andcomputes the preliminary false acceptance probability P_(PRE) based onthe difference data 131 (step S4). The false acceptance probabilitycomputation unit 33 also computes the false acceptance probabilityP_(FAR) using the computed false correspondence probability P_(FCR) andthe computed preliminary false acceptance probability P_(PRE) (step S5).Then, data indicating the false acceptance probability P_(FAR) is outputfrom the output unit 4 (step S6).

Advantages of the pattern recognition method, system and programaccording to this embodiment are as follows. The pattern recognitionsystem 10 according to the present invention includes the falsecorrespondence probability computation unit 32 and the false acceptanceprobability computation unit 33, and computes the false acceptanceprobability P_(FAR) based on the false correspondence probabilityP_(FCR) and the preliminary false acceptance probability P_(PRE). Thisincreases the accuracy in computing the false acceptance probabilityP_(FAR) for the input pattern and the reference pattern. The falsecorrespondence probability P_(FCR) is the probability of existence of alarger number of corresponding characteristic points than the number ofcorresponding characteristic points n. The preliminary false acceptanceprobability P_(PRE) is the probability that the “quantity” based on thecandidate difference data Ds is smaller than the “quantity” based on theinput difference data Di. Examples of the “quantity” include the norm ofthe difference data.

The pattern recognition system 10 according to the present inventioncomputes the false acceptance probability P_(FAR) based not only on thepreliminary false acceptance probability P_(PRE) but also on the falsecorrespondence probability P_(FCR). That is, the number of correspondingcharacteristic points n is taken into consideration in computing thefalse acceptance probability P_(FAR). As apparent from the formulas 1and 5, the greater the number of corresponding characteristic points nis, the smaller the computed false acceptance probability P_(FAR) is.This increases the reliability of the system.

Furthermore, in the cases such as identifying a fingerprint orrecognizing an object outdoors, the characteristic quantities of thepatterns have almost similar nature. In such cases, the model shown inFIG. 12 is effective. That is, the data Di′ resulting from rearrangingthe components of the input difference data Di is also used to computethe preliminary false acceptance probability P_(PRE). Specifically, thepreliminary false acceptance probability P_(PRE) is computed as theprobability that each component of the candidate difference data Ds issmaller than the corresponding component of the input difference data Dior the above-described data Di′. This further increases the accuracy incomputing the false acceptance probability P_(FAR).

In addition, the probability density function p(Ds) of the candidatedifference data Ds is modeled with a uniform distribution function. Thisreduces the learning cost for the pattern recognition system 10.

Second Embodiment

FIG. 3 is a block diagram showing a configuration of the patternrecognition system according to a second embodiment of the presentinvention. In FIG. 3, components similar to those shown in FIG. 1 aregiven the same reference numerals, and the description thereof will beomitted as appropriate.

In FIG. 3, the pattern recognition system 10 a includes a pattern inputunit 1, a reference pattern input unit 2, a data processing section 3 a,an output unit 4 a, and a permissible false acceptance probability inputunit 5. The data processing section 3 a includes a difference datadetection unit 31, a false correspondence probability computation unit32, a false acceptance probability computation unit 33, and an identitydetermination unit 34.

In this embodiment, the false acceptance probability computation unit 33outputs the false acceptance probability data 133 indicating thecomputed false acceptance probability P_(FAR) to the identitydetermination unit 34. The permissible false acceptance probabilityinput unit 5 outputs permissible false acceptance probability data 151indicating a permissible false acceptance probability P_(PFAR) to theidentity determination unit 34. The permissible false acceptanceprobability P_(PFAR) is the false acceptance probability permitted bythe system, i.e., the threshold for pattern recognition.

The identity determination unit 34 receives the false acceptanceprobability data 133 and the permissible false acceptance probabilitydata 151 from the false acceptance probability computation unit 33 andthe permissible false acceptance probability input unit 5 respectively.The identity determination unit 34 then determines whether the falseacceptance probability P_(FAR) is smaller than the permissible falseacceptance probability P_(PFAR). If the false acceptance probabilityP_(FAR) is smaller than the permissible false acceptance probabilityP_(PFAR), the identity determination unit 34 determines that the inputpattern and the reference pattern are identical. If the false acceptanceprobability P_(FAR) is larger than the permissible false acceptanceprobability P_(PFAR), the identity determination unit 34 does notdetermine that the input pattern and the reference pattern areidentical. The identity determination unit 34 outputs determinationresult data 134 indicating this determination result to the output unit4 a.

The output unit 4 a receives the determination result data 134 from theidentity determination unit 34 and outputs output data 142 based on thedetermination result. For example, when the pattern recognition system10 a is applied to a door control, a door is controlled to openaccording to the output data 142. When the pattern recognition system 10a is applied to an ATM of a bank, an operation by a customer is acceptedor rejected according to the output data 142. Examples of the patterndata 111 that is input to the pattern input unit include image dataabout the customer's fingerprint, face, and iris.

For example, when a four-digit personal identification number (PIN) isused for authentication of a person as a common practice, theprobability of false authentication in the system (hereafter referred toas the security strength) is one ten-thousandth. To achieve the samesecurity strength in the pattern recognition system 10 a according tothe present invention, the permissible false acceptance probabilityP_(PFAR) may be set to one ten-thousandth. Thus, the permissible falseacceptance probability P_(PFAR) (the threshold) directly means thesecurity strength of the system. The permissible false acceptanceprobability P_(PFAR) may be set in advance to a constant value in thedata processing section 3 a.

The above-described processing in the data processing section 3 a may beperformed by a computer. That is, the processing in the difference datadetection unit 31, the false correspondence probability computation unit32, the false acceptance probability computation unit 33, and theidentity determination unit 34 is each written as a computer program andexecuted by a computer.

FIG. 4 is a flowchart showing the summary of the pattern recognitionmethod according to the second embodiment of the present invention.First, the pattern data 111 is input to the pattern input unit 1 (stepS1). Then, the difference data detection unit 31 detects the differencedata 131 based on the pairs of characteristic quantities of the patterndata 111 and the reference pattern data 112 (step S2). Then, the falsecorrespondence probability computation unit 32 receives the differencedata 131 and computes the false correspondence probability P_(FCR) basedon the difference data 131 (step S3). Then, the false acceptanceprobability computation unit 33 receives the difference data 131 andcomputes the preliminary false acceptance probability P_(PRE) based onthe difference data 131 (step S4). The false acceptance probabilitycomputation unit 33 also computes the false acceptance probabilityP_(FAR) using the computed false correspondence probability P_(FCR) andthe computed preliminary false acceptance probability P_(PRE) (step S5).Then, the identity determination unit 34 compares the permissible falseacceptance probability P_(PFAR) and the computed false acceptanceprobability P_(FAR) to determine whether the false acceptanceprobability P_(FAR) is smaller than the permissible false acceptanceprobability P_(PFAR) (step S7). The output data 142 indicating thedetermination result is output from the output unit 4 (step S8).

In addition to the advantages of the first embodiment, the patternrecognition system 10 a according to the present embodiment provides thefollowing advantages. In the pattern recognition system 10 a, thethreshold applied to all input patterns is set to be constant.Furthermore, the applied threshold directly means the permissible falseacceptance probability P_(PFAR), i.e., the security strength of thesystem. This ensures stable security strength. This directcorrespondence of the threshold to the security strength is desirablefrom the viewpoint of system operation.

The above-described pattern recognition systems 10 and 10 a areapplicable to speech recognition, fingerprint identification, opticalcharacter recognition, signal recognition, and so forth.

Regardless of the above-described embodiments, various modifications maybe made to the pattern recognition system, pattern recognition method,and pattern recognition program according to the present inventionwithout departing from the spirit thereof. As an example, since theinput pattern and the reference pattern are treated as a pair, thepattern data 111 may be input to the reference pattern input unit 2 andthe reference pattern data 121 may be input to the pattern input unit 1.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a configuration of a patternrecognition system according to a first embodiment of the presentinvention;

FIG. 2 is a flowchart showing a pattern recognition method according tothe first embodiment of the present invention;

FIG. 3 is a block diagram showing a configuration of a patternrecognition system according to a second embodiment of the presentinvention;

FIG. 4 is a flowchart showing a pattern recognition method according tothe second embodiment of the present invention;

FIG. 5 is a conceptual view showing a distribution of characteristicpoints in an input pattern;

FIG. 6 is a conceptual view showing a distribution of characteristicpoints in a reference pattern;

FIG. 7 is a conceptual view showing determination of correspondencebetween the characteristic points by a difference data detection unit;

FIG. 8 is a conceptual view showing a distribution of correspondingcharacteristic points;

FIG. 9 is a graph showing an exemplary distribution of candidatedifference data;

FIG. 10 is a graph showing another exemplary distribution of candidatedifference data;

FIG. 11 is a graph showing still another exemplary distribution ofcandidate difference data; and

FIG. 12 is a graph showing still another exemplary distribution ofcandidate difference data.

DESCRIPTION OF SYMBOLS

-   1 pattern input unit-   2 reference pattern input unit-   3 data processing section-   4 output unit-   5 permissible false acceptance probability input unit-   10 pattern recognition system-   31 difference data detection unit-   32 false correspondence probability computation unit-   33 false acceptance probability computation unit-   34 identity determination unit-   111 pattern data-   112 pattern characteristic data-   121 reference pattern data-   122 reference pattern characteristic data-   131 difference data-   132 false correspondence probability data-   133 false acceptance probability data-   134 determination result data-   141 result data-   142 output data-   151 permissible false acceptance probability data

1. A pattern recognition system comprising: means for determining afirst vector including difference components between pairs ofcorresponding characteristic points indicating points correspondingbetween characteristic points in a first pattern and characteristicpoints in a second pattern; means for computing, based on the firstvector, a second probability indicating the probability of existence ofa second vector having difference components between pairs ofcorresponding characteristic points determined between the first patternand any pattern, wherein an amount based on the difference components ofthe second vector is smaller than an amount based on the differencecomponents in the first vector; and means for computing, based on thesecond probability, a false acceptance probability indicating theprobability of falsely determining that the first pattern and the secondpattern correspond to each other.
 2. The pattern recognition systemaccording to claim 1, wherein the second probability indicates theprobability that the magnitude of the second vector is smaller than themagnitude of the first vector.
 3. The pattern recognition systemaccording to claim 1, wherein the second probability indicates theprobability that the product of the components of the second vector issmaller than the product of the components of the first vector.
 4. Thepattern recognition system according to claim 1, wherein the secondprobability indicates that each component of the second vector issmaller than the corresponding component of the first vector.
 5. Thepattern recognition system according to claim 1, wherein a third vectorhas components resulting from rearranging the components of the firstvector, and the second probability indicates the probability of logicalOR between the event that each component of the second vector is smallerthan the corresponding component of the first vector and the event thateach component of the second vector is smaller than the correspondingcomponent of the third vector.
 6. The pattern recognition systemaccording to claim 1, wherein a distribution function of the secondvector is uniform.
 7. The pattern recognition system according to claim1, further comprising a difference data detection unit, wherein thedifference data detection unit receives first characteristic dataindicating the characteristic points in the first pattern and secondcharacteristic data indicating the characteristic points in the secondpattern, detects the corresponding characteristic points based on thefirst characteristic data and the second characteristic data, andcomputes difference data indicating the detection result.
 8. The patternrecognition system according to claim 1, further comprising anidentification determination unit, wherein the identificationdetermination unit compares the false acceptance probability and apredetermined threshold, and outputs a comparison result.
 9. The patternrecognition system according to claim 1, wherein the first pattern andthe second pattern are fingerprint patterns.
 10. A pattern recognitionmethod comprising the steps of: determining a first vector includingdifference components between pairs of corresponding characteristicpoints indicating points corresponding between characteristic points ina first pattern and characteristic points in a second pattern; using thefirst vector to compute a second probability indicating the probabilityof existence of a second vector having difference components betweenpairs of corresponding characteristic points determined between thefirst pattern and any pattern, wherein an amount based on the differencecomponents of the second vector is smaller than an amount based on thedifference components in the first vector; and referring to the secondprobability to compute a false acceptance probability indicating theprobability of falsely determining that the first pattern and the secondpattern correspond to each other.
 11. The pattern recognition methodaccording to claim 10, wherein the second probability indicates theprobability that the magnitude of the second vector is smaller than themagnitude of the first vector.
 12. The pattern recognition methodaccording to claim 10, wherein the second probability indicates theprobability that the product of the components of the second vector issmaller than the product of the components of the first vector.
 13. Thepattern recognition method according to claim 10, wherein the secondprobability indicates the probability that each component of the secondvector is smaller than the corresponding component of the first vector.14. The pattern recognition method according to claim 10, wherein thethird vector has components resulting from rearranging the components ofthe first vector, and the second probability indicates the probabilityof logical OR between the event that each component of the second vectoris smaller than the corresponding component of the first vector and theevent that each component of the second vector is smaller than thecorresponding component of the third vector.
 15. The pattern recognitionmethod according to claim 10, further comprising the steps of: comparingthe false acceptance probability and a predetermined threshold; anddetermining that the first pattern and the second pattern are identicalif the false acceptance probability is smaller than the predeterminedthreshold.
 16. A computer-readable medium storing a computer code forcausing a computer to perform the steps of: determining a first vectorincluding difference components between pairs of correspondingcharacteristic points indicating points corresponding betweencharacteristic points in a first pattern and characteristic points in asecond pattern; using the first vector to compute a second probabilityindicating the probability of existence of a second vector havingdifference components between pairs of corresponding characteristicpoints determined between the first pattern and any pattern, wherein anamount based on the difference components of the second vector issmaller than an amount based on the difference components in the firstvector; and referring to the second probability to compute a falseacceptance probability indicating the probability of falsely determiningthat the first pattern and the second pattern correspond to each other.17. The computer-readable medium according to claim 16, wherein thesecond probability indicates the probability that the magnitude of thesecond vector is smaller than the magnitude of the first vector.
 18. Thecomputer-readable medium according to claim 16, wherein the secondprobability indicates the probability that the product of the componentsof the second vector is smaller than the product of the components ofthe first vector.
 19. The computer-readable medium according to claim16, wherein the second probability indicates the probability that eachcomponent of the second vector is smaller than the correspondingcomponent of the first vector.
 20. The computer-readable mediumaccording to claim 16, wherein a third vector has components resultingfrom rearranging the components of the first vector, and the secondprobability indicates the probability of logical OR between the eventthat each component of the second vector is smaller than thecorresponding component of the first vector and the event that eachcomponent of the second vector is smaller than the correspondingcomponent of the third vector.
 21. The computer-readable mediumaccording to claim 20, further storing computer code for causing acomputer to perform the following: comparing the false acceptanceprobability and a predetermined threshold; and determining that thefirst pattern and the second pattern are identical if the falseacceptance probability is smaller than the predetermined threshold. 22.The computer-readable medium according to claim 16, further storingcomputer code for causing the computer to perform the following:receiving first characteristic data indicating the characteristic pointsin the first pattern and second characteristic data indicating thecharacteristic point in the second pattern, detecting the correspondingcharacteristic points based on the first characteristic data and thesecond characteristic data, and computing difference data indicating thedetection result, the difference data including the number of thecharacteristic points in the first pattern, the number of thecharacteristic points in the second pattern, the number of thecorresponding characteristic points, and the first vector; and computingthe false acceptance probability based on the difference data.
 23. Thecomputer-readable medium according to claim 22, further storing computercode for causing a computer to perform the following: comparing thefalse acceptance probability and a predetermined threshold; anddetermining that the first pattern and the second pattern are identicalif the false acceptance probability is smaller than the predeterminedthreshold.
 24. The computer-readable medium according to claim 16,further storing computer code for causing a computer to perform thefollowing: comparing the false acceptance probability and apredetermined threshold; and determining that the first pattern and thesecond pattern are identical if the false acceptance probability issmaller than the predetermined threshold.
 25. The computer-readablemedium according to claim 20, further storing computer code for causinga computer to perform the following: receiving first characteristic dataindicating the characteristic points in the first pattern and secondcharacteristic data indicating the characteristic point in the secondpattern, detecting the corresponding characteristic points based on thefirst characteristic data and the second characteristic data, andcomputing difference data indicating the detection result, thedifference data including the number of the characteristic points in thefirst pattern, the number of the characteristic points in the secondpattern, the number of the corresponding characteristic points, and thefirst vector; and computing the false acceptance probability based onthe difference data.